The Use of Extrinsic Evidence in the Interpretation of Written Agreements in Alberta
Bibliographic record
Abstract
The main objective of the court when attempting to interpret a written agreement is to give effect to the true intention of the parties to that agreement. To do this, the court first looks to the words compiling the agreement to attempt to give a fair and plain meaning to it. However, when the agreement after considering the plain and ordinary meaning of the words therein is still not clear, tire court may feel Justified in using extrinsic evidence, such as the circumstances surrounding the parties when coming to the agreement, to find and give effect to their true intentions. The use of extrinsic evidence to interpret a written agreement must be limited to situations where the intentions of the parties are unclear after looking at the written agreement on its own. Various rules and principles complicate this basic underlying statement. They exist to ensure the court does not simply transpose its "view” of what is fair and reasonable in lieu of contractual interpretation. This article attempts to outline these various rules and principles as they exist in the law of extrinsic evidence when interpreting contracts in Alberta.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.021 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".